Spectral properties of muscle activation during incremental cycling test
Bibliographic record
Abstract
It is uncertain whether fatigue and workload would affect muscle recruitment during cycling. To infer on changes in priority for recruitment of motor units, we assessed the combined effects of fatigue and workload during an incremental cycling test to exhaustion on frequency components of lower limbs muscle activation. Competitive cyclists performed an incremental maximal cycling test while muscle activations were recorded from the right and left vastus lateralis, biceps femoris and gastrocnemius medialis. Muscle activation signals were assessed using frequency band analyses and decomposed into overall, high and low frequency bands. Combined effects from workload/fatigue were assessed using t tests and Cohen’s effect sizes (ES). There were increases in the overall muscle activation due to increased workload/fatigue for biceps femoris (40% vs. 90%, p<0.01 and ES = 1.85) and vastus lateralis (40% vs. 70%, p=0.01 and ES = 0.96, and 40% vs. 90%, p<0.01 and ES = 2.03, and 70% vs. 90%, p<0.01 and ES = 0.96), but not for gastrocnemius medialis. There was also greater contribution from low frequency component for biceps femoris (40% vs. 90%, p = 0.01 and ES = 1.12). Similar workload/fatigue effects have been observed between lower limbs. In conclusion, incremental cycling test lead to an increase in activation of main knee joint flexors and extensors but not in plantar flexors during cycling. Biceps femoris changes its recruitment profile due to increases in low frequency content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".